These data augmentation or regularization based approaches represent SPIRE's most direct competition and, as a result, we compare against “Right for the Right Reasons” (RRR) [1]}, “Quantifying and Controlling the Effects of Context” (QCEC) [2]}, “Contextual Decomposition Explanation Penalization” (CDEP) [3]}, “Gradient Supervision” (GS) [4]}, and the “Feature Splitting” (FS) method from [5]} because they all directly apply to image classification.
We note that there are two additional lines of work that make different assumptions.
Making weaker assumptions, there are methods based on sub-sampling, re-weighting, or grouping the training set [6]}, [7]}, [8]}.
These methods have been found to be less effective than methods that use data augmentation or regularization [3]}, [10]}, [5]}, [12]}.
Making stronger assumptions, there are methods from domain adaptation, which assume access to several distinct training sets [13]}, [14]}; we do not assume this.
